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In the context of systems and cybernetics theory, we present a new general stochastic method of search and optimization of solutions of problems that we have named Prototyped Genetic Search. Our new method is based mainly on prototype and learning concepts, although it uses concepts of population and evolution just as Evolutionary Algorithms. Moreover, and in order to show the interest of this method and to demonstrate its real potential, we have chosen to apply it on the Job‐Shop Scheduling Problem in the context of the flexible production. This paper is also the opportunity for us to present an other new kind of genetic algorithms, resulting from the integration of the recursivity in the basis functioning of genetic algorithms, and that we have named Recursive Genetic Algorithm.

Discusses recursive estimation techniques which can be used to update or revise estimates of the parameters of an economic model to account for new data. Such methods admit the possibility of proceeding with the gathering of observed data until a specified accuracy of the parameters is achieved or if the economic processes are time‐varying the parameters can be tracked. Recursive methods can also be used for adaptive learning, forecasting and control. Examines both single equation ‐ static as well as dynamic ‐ economic models.

An identification scheme to identify interconnected discrete-time (DT) varying systems.

The purpose of this paper is the identification of interconnected discrete time varying systems. The proposed technique permits the division of global system to many subsystems by building a vector observation of each subsystem and then using the gradient method to identify the time-varying parameters of each subsystem. The convergence of the presented algorithm is proven under a given condition.

The effectiveness of the proposed technique is then shown with application to a simulation example.

In the past decade, there has been a renewed interest in interconnected systems that are multidimensional and composed of similar subsystems, which interact with their closest neighbors. In this context, the concept of parametric identification of interconnected systems becomes relevant, as it considers the estimation problem of such systems. Therefore, the identification of interconnected systems is a challenging problem in which it is crucial to exploit the available knowledge about the interconnection structure. For time-varying systems, the identification problem is much more difficult. To cope with this issue, this paper addresses the identification of DT dynamical models, composed by the interconnection of time-varying systems.

The purpose of this paper is to deal with a problem of admission control in computer networks when some of their parameters are uncertain. The case is considered when the most common probabilistic description of the uncertainty cannot be used and another approach should be applied.

The uncertain versions of admission control problem with quality of service requirements are considered. The uncertain variables are used to describe possible values of the unknown parameters in computer networks.

Given are formulations for the admission control problem in computer networks with unknown values of the capacities based on the network utility maximization concept. Solution algorithms for all these problems are proposed.

It is assumed that an expert can describe possible values of uncertain network parameters in the form of a certainty distribution. Then the formalism of uncertain variables is applied and the knowledge of an expert is modelled with the use of certainty distributions. Decisions strongly depends on the quality of an expert's knowledge.

Obtained admission control algorithms can be useful for planning and designing of computer networks.

A new approach to the admission control problem in computer networks in the presence of uncertainty, in the case when the uncertain variable can be applied, is proposed and discussed.

This paper deals with bispectrum estimation via autoregressive (AR) modelling of a process contaminated by additive Gaussian noise (white and coloured). Two main contributions are provided in this work. First, a comparison between the existing third order recursion (TOR) and the constrained third order mean (CTOM) methods is presented. Basically, the second method is shown to be a smoothing windowed version (i.e. a covariance‐type estimator) of the first one, achieved at the expense of the loss of the recursivity in the AR‐model order. This prior analysis has induced us to develop an alternative scheme to tackle this type of problem, which, while maintaining the main feature of the CTOM method as a covariance type estimator, is a recursive‐in‐order algorithm. This recursivity is obtained carrying out an appropriate minimization procedure of some prediction squared errors also defined here. The paper also compares, by means of simulations, this proposed method and the two existing ones.

The purpose of this paper is to propose an identification algorithm to obtain generalized attitude model (GAM) of satellites in on‐orbit environment, which includes missing attitude data and multi‐noise. The identified GAM and noise model are the basis of attitude control and state estimation on‐orbit.

To cope with noises contaminating both input and output of attitude model, the errors‐in‐variables model is transformed into a traditional Box‐Jenkins model according to the attitude control loop. The wavelet denoising (WD) technique is helpful to predict the missing output data using the identified GAM.

By the numerical simulation, it is verified that the proposal accompanied with WD has a faster prediction capability than that of the algorithm without WD. As a result, the proposed approach is suitable to attitude model identification of on‐orbit satellites.

This identification algorithm can deal with two kinds of on‐orbit conditions and has a fast parameter convergent rate. Therefore, it has a practical application value in on‐orbit environment.

Most of the conventional humanoid modeling approaches are not successful in coupling different branches of the tree-type humanoid robot. In this paper, a tree-type upper body humanoid robot with mobile base is modeled. The main purpose of this work is to model a non holonomic mobile platform and to develop a hybrid algorithm for avoiding dynamic obstacles. Decoupled Natural Orthogonal Complement methodology effectively combines different branches of the humanoid body during dynamic analysis. Collision avoidance also plays an important role along with modeling methods for successful operation of the upper body wheeled humanoid robot during real-time operations. The majority of path planning algorithms is facing problems in avoiding dynamic obstacles during real-time operations. Hence, a multi-fusion approach using a hybrid algorithm for avoiding dynamic obstacles in real time is introduced.

The kinematic and dynamic modeling of a humanoid robot with mobile platform is done using screw theory approach and Newton–Euler formulations, respectively. Dynamic obstacle avoidance using a novel hybrid algorithm is carried out and implemented in real time. D star lite and a geometric-based hybrid algorithms are combined to generate the optimized path for avoiding the dynamic obstacles. A weighting factor is added to the D star lite variant to optimize the basic version of D star lite algorithm. Lazy probabilistic road map (PRM) technique is used for creating nodes in configuration space. The dynamic obstacle avoidance is experimentally validated to achieve the optimum path.

The path obtained using the hybrid algorithm for avoiding dynamic obstacles is optimum. Path length, computational time, number of expanded nodes are analysed for determining the optimality of the path. The weighting function introduced along with the D star lite algorithm decreases computational time by decreasing the number of expanding nodes during path generation. Lazy evaluation technique followed in Lazy PRM algorithm reduces computational time for generating nodes and local paths.

Modeling of a tree-type humanoid robot along with the mobile platform is combinedly developed for the determination of the kinematic and dynamic equations. This paper also aims to develop a novel hybrid algorithm for avoiding collision with dynamic obstacles with minimal computational effort in real-time operations.

This paper employs new methods to evaluate the availability of multistate series–parallel systems, in which a number of similar components are available in each subsystem.

In this paper, polynomial distribution function (PDF) is combined with universal generating function (UGF) and recursive algorithm (RA) methods to evaluate the availability of multistate series–parallel systems. To achieve this goal, the PDF is initially used to determine the performance rates and the probabilities corresponding to the performance states of the similar components in a subsystem. The obtained results are used to evaluate the system availability via the UGF and RA methods.

It is shown that the combined UGF and PDF (UGF-PDF) and also the combined RA and PDF (RA-PDF) methods require less computational time than did the UGF and RA methods, respectively.

In the UGF and RA methods, there is no difference in system availability evaluation time when considering similar or different components in each subsystem. But the proposed methods in this article do not have this restrictions; therefore, these methods can be used to evaluate system availability in optimal redundancy allocation problems. As a result, using these methods reduces the optimization time of those problems.

When a manipulator captures an unknown space object, inertia parameters of endpoint payload should be timely obtained to handle possible unexpected parameter variations and monitor the system’s operating conditions. Therefore, this study aims to present an identification method for estimating the inertia parameter of the payload carried by a flexible two-link space manipulator.

The original nonlinear dynamics model of the manipulator is linearized at a selected working point. Subsequently, the system modal frequencies with and without payload are determined using the subspace identification algorithm, and the difference of these frequencies is computed. Furthermore, by adjusting the structural configuration of the manipulator, multiple sets of frequency differences are obtained. Therefore, the inertia parameters of the payload, i.e. the mass and the moment of inertia, can be derived from the frequency differences by solving a least-squares problem.

The proposed method can effectively estimate the payload parameters and has satisfactory identification accuracy.

The approach’s implementation provides a practical reference for determining inertia parameters of an unknown space target in the capture process.

The study proposes a novel method for identifying the inertia parameters of the payload of a flexible two-link space manipulator using the estimated system frequencies.

This paper aims to improve the semantic-disambiguation capability of an information-retrieval system by taking advantages of a well-crafted classification tree. The unstructured nature and sheer volume of information accessible over networks have made it drastically difficult for users to seek relevant information. Many information-retrieval methods have been developed to address this problem, and keyword-based approach is amongst the most common approach. Such an approach is often inadequate to cope with the conceptualization associated with user needs and contents. This brings about the problem of semantic ambiguation that refers to the disagreement in meaning of terms between involving parties of a communication due to polysemy, leading to increased complexity and lesser accuracy in information integration, migration, retrieval and other related activities.

A novel ontology-based search approach, named GeTFIRST (short for Graph-embedded Tree Fostering Information Retrieval SysTem), is proposed to disambiguate keywords semantically. The contribution is twofold. First, a search strategy is proposed to prune irrelevant concepts for accuracy improvement using our Graph-embedded Tree (GeT)-based ontology. Second, a path-based ranking algorithm is proposed to incorporate and reward the content specificity.

An empirical evaluation was performed on United States Patent And Trademark Office (USPTO) patent datasets to compare our approach with full-text patent search approaches. The results showed that GeTFIRST handled the ambiguous keywords with higher keyword-disambiguation accuracy than traditional search approaches.

The search approach of this paper copes with the semantic ambiguation by using our proposed GeT-based ontology and a path-based ranking algorithm.